Use of Artificial Intelligence and Data Analytics in the Life Insurance Industry

Insurance CIO Outlook | Tuesday, January 03, 2023

Many life insurers embrace machine learning (ML) and artificial intelligence (AI) models and techniques.

FREMONT, CA: In many aspects of their operations, life insurers adopt machine learning (ML) and artificial intelligence (AI) models and techniques. Historically, the non-life sector has demonstrated greater use of data science methodologies, and life insurance is increasingly utilizing cutting-edge methods. Life insurers demonstrate a strong commitment to expediting digital transformation from fraud detection to underwriting simplification.

This article discusses some of the applications of data science in life insurance:


ML and AI have been utilized in the underwriting process for large insurers. Some insurers utilize these techniques to automate the underwriting process, enabling acceptance decisions to be made in minutes, while others use AI models to analyze enormous data files rapidly. More automated underwriting attempts to increase client satisfaction in their dealings with their insurer and expedite policy acceptance decisions.

Pricing and design of products

By boosting customer interaction and well-being efforts, insurers have shifted their attention from traditional financial-oriented goals to a broader ecosystem approach. Insurers have incentivized sharing data from wearables by offering discounts and rewards for quitting smoking or measuring more than 10,000 steps.

ML approaches can increase the speed and accuracy of risk assessments in underwriting, as well as improve the segmentation of customer risk profiles and update pricing assumptions based on new data.

Sales and marketing

Robo-advisors have arisen to automate acquiring insurance, helping customers, and recommending relevant policies. This form of automated guidance works well with essential products and can be used to promote products to existing clients at all phases of their lives.

Additionally, interactions with customer support and sales personnel can be captured to extract value that can enhance modeling or better comprehend clients.

Claims management

The expense of claims administration is significant for insurers. Claims for life insurance frequently include unstructured data that must be analyzed using techniques such as natural language processing to expedite claims processing.

Fraud detection

Insurers now have various methods for detecting fraud, such as examining a claimant's online presence for signs of smoking and drug usage. Fraud committed by clients who provide fraudulent documents or misrepresent their health, family background, or career can be detected using machine learning and analytics.

The possibility of identity theft or account takeover is increasing, and machine learning techniques can be used to detect unusual account activity. Utilizing anomaly detection techniques, fraudsters who gain access to existing consumer accounts have been identified.

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